Semiconductor & Technology Specialist, Ingenuity

William Keating

Dr. William Keating is a highly experienced senior executive with over 25 years experience in the IT/Semiconductor Industry. The majority of Dr. Keating's career was spent at Intel Corporation where he held senior leadership positions in Europe, the US and Asia from 1992 through 2014. He served as Intel's IT Director for APAC, China & Japan from 2005 through 2014 when he was based in Hong Kong and was responsible for the IT systems (Infrastructure, Networks, Data Centres, ERP, Sales & Marketing systems, Business Intelligence etc. ) for all of Intel's offices & factories through the region. From 1998 through 2005, he was the founder & director of an internal IT Consulting Organisation (IT Flex Services), a 500-person team which remains a core part of the Intel IT Organisation to this day. Dr. Keating's first 6 years at Intel were spent in charge of the Factory Automation Systems (Robotics, Databases, Networks, Servers, Clients etc.) supporting Intel's then state-of-the-art 200mm factory network. Since leaving Intel in 2014, Dr Keating established his own Technology Research & Consulting Company (www.ingenuity.hk) where he focuses on the products, roadmaps & technologies of companies such as Intel, AMD, Samsung, Apple, Micron- as well as the key equipment suppliers to those companies e.g. ASML, AMAT, Canon, Nikon etc. Dr. Keating specializes in niche topics of high significance within the semiconductor industry. These include the future of Moore's Law and in particular the significance of Lithography in maintaining Moore's Law (EUV, SDA, NanoImprint), the role & growth of the GPU in specialised applications such as Deep Learning (Neural Networks) for Voice, Image & Pattern recognition at companies such as Facebook, Google & Baidu. He also holds regular discussions on the future of memory, in particular the rise of 3D NAND and the emergence of new memory technologies such as 3D Xpoint.

Semiconductor & Technology Specialist

Google Is Running Out of Money! Just Kidding! AI Capex Financing Versus Token Cost Decline.

Alphabet Q1 2026. Enterprise AI Demand Now The Primary Growth Driver For Google Cloud

Broadcom: The Stock Is Falling -14% ! How Bad Was It? Not Really Bad But…

Semiconductor Market Value. TSMC Said $1.5tn by 2030. WSTS Says $1.9tn in '27. $2.5 Trillion by '30?

Ingenuity

Our focus is exclusively on in-depth research & consulting in the broader semiconductor industry. We focus on the key players, namely Intel, Micron, Qualcomm, Apple, Samsung, NVIDIA, AMD, TSMC & Global Foundries - as well as some of the lesser known, but nonetheless critical, companies such as ASML (for EUV), Mellanox, Cavium, Xilinx etc. We specialise in their products, roadmaps, process technologies, competitive advantages & threats etc. We also help our clients understand the implications of new and emerging technologies, e.g. 3D X-Point, 3D NAND, Carbon Nanotubes, Autonomous Driving, Graphene, Silicon Photonics, Wafer-Level Optics, Artificial Intelligence, FPGA, GPU, ASIC etc. 

Semiconductor Analysis & Research

Equity Bottom-Up

<ul>
<li>
<strong>Model Convergence</strong>: capabilities that once required specialized models are increasingly handled by the main Gemini LLMs, which can now be state of the art for math and other scientific questions</li>
<li>Brin admitted he is “always kind of confused as to how to prompt the models” and even the Gemini team does not know exactly where the models’ capability edges are!</li>
<li>He cited chain-of-thought (”think step by step”) as an example of a technique that seemed trivial or “the dumbest thing ever” yet drove a significant jump in AI capability.</li>
</ul>



Model Convergence: capabilities that once required specialized models are increasingly handled by the main Gemini LLMs, which can now be state of the art for math and other scientific questions
Brin admitted he is “always kind of confused as to how to prompt the models” and even the Gemini team does not know exactly where the models’ capability edges are!
He cited chain-of-thought (”think step by step”) as an example of a technique that seemed trivial or “the dumbest thing ever” yet drove a significant jump in AI capability.


Sergey Brin On Model Convergence, Prompt Engineering & Transformer Evolution